Topology selection for stream mining systems
Pages 113 - 120
Abstract
Multi-concept identification in high volume multimedia streams is critical for a number of applications, including large-scale multimedia analysis, processing, and retrieval. Content of interest is filtered using a collection of binary classifiers that are deployed on distributed resource-constrained infrastructure. In this paper, we design distributed algorithms for determining the optimal topology of single concept detectors (classifiers) to identify the multiple concepts of interest. These algorithms dynamically order individual classifiers into chain topologies to tradeoff accuracy against processing delay, based on underlying data characteristics, system resource constraints as well as the performance and complexity characteristics of each classifier
References
[1]
B. Babcock, S. Babu, R. Motwani, and M. Datar. Chain: operator scheduling for memory minimization in data stream systems. In ACM SIGMOD, 2003.
[2]
S. Babu, R. Motwani, K. Munagala, I. Nishizawa, and J. Widom. Adaptive ordering of pipelined stream filters. ACM SIGMOD, 2004.
[3]
R. Ducasse, D. S. Turaga, and M. van der Schaar. Adaptive topologic optimization for large-scale stream mining. IEEE Journal on Selected Topics in Signal Processing, under review.
[4]
F. Fu, D. S. Turaga, O. Verscheure, M. van der Schaar, and L. Amini. Configuring competing classifier chains in distributed stream mining systems. IEEE Journal on Selected Topics in Signal Processing, 2007.
[5]
R. Lienhart, L. Liang, and A. Kuranov. A detector tree of boosted classifiers for real-time objects detection and tracking. In IEEE ICME, 2003.
[6]
H. Park, D. S. Turaga, O. Verscheure, and M. van der Schaar. Foresighted tree configuring games in resource constrained distributed stream mining systems. IEEE ICASSP, 2009.
[7]
L. Saul and M. I. Jordan. Learning in boltzman trees. Neural Computation, 1994.
[8]
R. S. Sutton and A. G. Barto. Reinforcement learning: An introduction. The MIT Press, Cambridge, MA, 1998.
[9]
V. Vazirani. Approximation algorithms. Springer Verlag.
Index Terms
- Topology selection for stream mining systems
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Published In
October 2009
144 pages
ISBN:9781605587561
DOI:10.1145/1631058
- General Chairs:
- Rong Yan,
- Qi Tian,
- John R. Smith,
- Rahul Sukthankar
Copyright © 2009 ACM.
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Publication History
Published: 23 October 2009
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